Recently, deep learning methods applied on point clouds have proven successful for several tasks [1]}, [2]}, [3]}, [4]}.
In particular, PointNet [1]} paved the way for neural networks that directly use 3D point clouds for object classification and segmentation purposes, and has been extended towards learning point cloud features in metric space [2]}.
This extension increased the effectiveness of PointNet to learn features on different scales, in the manner of traditional convolutional networks.
Other approaches rely on the spherical projection of point clouds, usually coined as range (or depth) image – which is, in most cases, the original structure of the sensor data.
This is used in the LiDAR odometry techniques [7]}, [8]}, [9]} which makes use of the entire scan to estimate the motion between two frames in an end-to-end manner.
